Common horizontal bounding box-based methods are not capable of accurately locating slender ship targets with arbitrary orientations in synthetic aperture radar (SAR) images. Therefore, in recent years, methods based on oriented bounding box (OBB) have gradually received attention from researchers. However, most of the recently proposed deep learning-based methods for OBB detection encounter the boundary discontinuity problem in angle or key point regression. In order to alleviate this problem, researchers propose to introduce some manually set parameters or extra network branches for distinguishing the boundary cases, which make training more difficult and lead to performance degradation. In this article, in order to solve the boundary discontinuity problem in OBB regression, we propose to detect SAR ships by learning polar encodings. The encoding scheme uses a group of vectors pointing from the center of the ship target to the boundary points to represent an OBB. The boundary discontinuity problem is avoided by training and inference directly according to the polar encodings. In addition, we propose an intersect over union (IOU)-weighted regression loss, which further guides the training of polar encodings through the IOU metric and improves the detection performance. Comparative experiments on the benchmark Rotating SAR Ship Detection Dataset (RSSDD) demonstrate the effectiveness of our proposed method in terms of enhanced detection performance over state-of-the-art algorithms and other OBB encoding schemes.

中文翻译：

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学习极性编码以用于SAR图像中的任意方向的舰船检测

常见的基于水平边界框的方法无法在合成孔径雷达（SAR）图像中准确定位任意方向的细长船目标。因此，近年来，基于定向边界框（OBB）的方法逐渐受到研究人员的关注。但是，大多数最近提出的基于深度学习的OBB检测方法都遇到角度或关键点回归中的边界不连续性问题。为了缓解这个问题，研究人员建议引入一些手动设置的参数或额外的网络分支来区分边界情况，这会使训练更加困难并导致性能下降。在本文中，为了解决OBB回归中的边界不连续性问题，我们建议通过学习极坐标编码来检测SAR船舶。编码方案使用一组从飞船目标中心指向边界点的向量来表示OBB。通过直接根据极坐标编码进行训练和推理，可以避免边界不连续性问题。此外，我们提出了相交于联合（IOU）加权回归损失，它进一步通过IOU度量标准指导了极性编码的训练，并提高了检测性能。在基准旋转SAR舰船检测数据集（RSSDD）上进行的比较实验证明，相对于最新算法和其他OBB编码方案，我们提出的方法在增强检测性能方面是有效的。通过直接根据极坐标编码进行训练和推理，可以避免边界不连续性问题。此外，我们提出了相交于联合（IOU）加权回归损失，它进一步通过IOU度量标准指导了极性编码的训练，并提高了检测性能。在基准旋转SAR舰船检测数据集（RSSDD）上进行的比较实验证明，相对于最新算法和其他OBB编码方案，我们提出的方法在增强检测性能方面是有效的。通过直接根据极坐标编码进行训练和推理，可以避免边界不连续性问题。此外，我们提出了相交于联合（IOU）加权回归损失，它进一步通过IOU度量标准指导了极性编码的训练，并提高了检测性能。在基准旋转SAR舰船检测数据集（RSSDD）上进行的比较实验证明，相对于最新算法和其他OBB编码方案，我们提出的方法在增强检测性能方面是有效的。